This section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present invention that are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present invention. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
Plenoptic cameras are a category of cameras that belongs to the family of light field acquisition device. A particular feature of this category of cameras is to use a lenslet array that is placed between a main lens and a photosensor. The architecture of a plenoptic camera is described in several documents such as the document WO2016046136, or in document US2013265485, or in document WO2014039327, or in the article entitled “The Focused Plenoptic Camera” by Andrew Lumsdaine and Todor Georgiev. Once a 4D light field data is acquired by a plenoptic camera, it is possible to perform refocusing and/or change of viewpoints aposteriori, without an excessive amount of processing operations. Hence, these kinds of devices seem to be more and more used in the future.
Thus, plenoptic camera calibration is a hectic research topic due to the widespread use of plenoptic cameras on the markets. Usually, in a calibration process, two kind of parameters can be estimated/determined: the extrinsic camera parameters and the intrinsic camera parameters. The extrinsic camera parameters relate to position and rotation of the model points in relation to a plenoptic camera, whereas the intrinsic camera parameters define/parametrize the projection of light rays through the plenoptic camera onto a photosensor comprised in the plenoptic camera.
In the state of the art, in order to determine intrinsic parameters, it is necessary to define a model that depicts the projection process onto the photosensor of a plenoptic camera. Usually, the definition of a model induces a number of intrinsic parameters to be determined, and the more accurate and complex a model is, the more intrinsic parameters it defines and uses.
A first model is described in the article entitled “On the Calibration of Focused Plenoptic Cameras” by Ole. Johannsen et al. where it is proposed to minimize an energy model based upon the thin lens equation. The model allows to estimate intrinsic and extrinsic parameters and corrects for radial lateral as well as radial depth distortion.
A second model is described in the article entitled “Unconstrained Two-parallel-plane Model for Focused Plenoptic Cameras Calibration” by Chunping Zhang et al., that uses 7 parameters to describe a 4D light field acquired by a plenoptic camera.
A third model is described in the article entitled “Metric Calibration of a focused plenoptic camera based on 3D calibration target” by N. Zeller et al., that considers lateral distortion of the intensity image as well as virtual depth distortion.
A fourth model is described in the article entitled “A Light Transport Framework for Lenslet Light Field Cameras” by Chia-Kai Liang and Ravi Ramamoorthi, that considers the full space-angle sensitivity profile of the photosensor within a plenoptic camera.
Once a model is defined according to one of the previously-mentioned articles, a calibration process is usually performed. In some cases, such calibration process comprises the solving of a non-linear optimization process with regards to a cost function. However, when the number of parameters in the model is important (for example, up to 21 parameters are described in the model described in the article “decoding, calibrating and rectification for lenslet-based plenoptic cameras”, by D. G. Dansereau et al.), the calibration process is complex from a computational point of view. In order to reduce the complexity of such calibration process, it was proposed in the article entitled “Geometric calibration of micro-lens based light field cameras using line features” by Y. Bok et al., to use a linear method for computing a first estimation of the parameters of the model, and then to refine the obtained results via a non-linear optimization process. However, this approach is still complex from a computational point of view.
Hence, there is a need for providing another model that can be both relevant in term of physical description of the projection process within a plenoptic camera, and for which the intrinsic parameters can be easily determined, from a computational point of view, via a calibration process.